Research on Conventional Beamforming Based on Compressive Sensing

Abstract

Compressive sensing, or compressive sampling (for short, CS) is a novel sensing/sampling paradigm. With the rapid development of the theory and algorithms for sparse recovery in finite dimensions, compressive sensing has already inspired some notable investigation in the context of Direction Of Arrival (DOA) estimation. In this paper, we show how CS can be applied in the DOA estimation and be solved by the well-established toolbox, CVX. In order to ensure a high spatial resolution, the conventional compressive beamforming formulation is further extended to a virtual array case. Numerical simulations illustrate the effectiveness of the DOA estimation algorithm based on CS. In addition, numerical tests also show that under some challenging scenarios such as low SNR, coherent arrivals and few snapshots, compressive beamforming based on virtually expanded array (for convenience, called V-CS) can distinguish closely spaced sources and has higher resolving probability than conventional compressive beamforming.